Despite the promises of data-driven artificial intelligence (AI), little is known about how we can bridge the gulf between traditional physician-driven diagnosis and a plausible future of medicine automated by AI. Specifically, how can we involve AI usefully in physicians' diagnosis workflow given that most AI is still nascent and error-prone (e.g., in digital pathology)? To explore this question, we first propose a series of collaborative techniques to engage human pathologists with AI given AI's capabilities and limitations, based on which we prototype Impetus - a tool where an AI takes various degrees of initiatives to provide various forms of assistance to a pathologist in detecting tumors from histological slides. Finally, we summarize observations and lessons learned from a study with eight pathologists and discuss recommendations for future work on human-centered medical AI systems.

中文翻译：

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为病理学家设计支持 AI 的诊断工具的经验教训

尽管数据驱动的人工智能 (AI) 前景广阔，但人们对如何弥合传统医生驱动的诊断与人工智能自动化医学的合理未来之间的鸿沟知之甚少。具体而言，鉴于大多数人工智能仍处于起步阶段且容易出错（例如，在数字病理学中），我们如何将人工智能有效地纳入医生的诊断工作流程？为了探讨这个问题，我们首先提出了一系列协作技术，根据 AI 的能力和局限性，让人类病理学家与 AI 互动，并在此基础上我们对 Impetus 进行原型设计——一种 AI 采取不同程度的主动性为患者提供各种形式的帮助的工具。病理学家从组织切片中检测肿瘤。最后，